Using corpus statistics on entities to improve semi-supervised relation extraction from the Web

ACL 2007 - Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics

Volumn , Issue , 2007, Pages 600-607

  Rosenfeld, Benjamin ^a   Feldman, Ronen ^a  

^a HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

CORPUS-BASED METHODS; CORRECTION METHOD; NAMED-ENTITY RECOGNITION; RELATION EXTRACTION; SEMI-SUPERVISED; SHALLOW PARSING;

COMPUTATIONAL LINGUISTICS;

EID: 80053498006     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

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2. Brin, S. (1998). Extracting Patterns and Relations from the World Wide Web. WebDB Workshop at 6th International Conference on Extending Database Technology, EDBT'98, Valencia, Spain.
3. Chen, J., D. Ji, C. L. Tan and Z. Niu (2005). Unsupervised Feature Selection for Relation Extraction. IJCNLP-05, Jeju Island, Korea.
4. Downey, D., M. Broadhead and O. Etzioni (2007). Locating Complex Named Entities in Web Text. IJCAI-07.
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6. Feldman, R. and B. Rosenfeld (2006). Boosting Unsupervised Relation Extraction by Using NER. EMNLP-06, Sydney, Australia.
7. Feldman, R. and B. Rosenfeld (2006). Self-Supervised Relation Extraction from the Web. ISMIS-2006, Bari, Italy.
8. Hasegawa, T., S. Sekine and R. Grishman (2004). Discovering Relations among Named Entities from Large Corpora. ACL 2004.
9. Ravichandran, D. and E. Hovy (2002). Learning Surface Text Patterns for a Question Answering System. 40th ACL Conference.
10. Riloff, E. and R. Jones (1999). Learning Dictionaries for Information Extraction by Multi-level Bootstrapping. AAAI-99.
11. Rosenfeld, B., M. Fresko and R. Feldman (2005). A Systematic Comparison of Feature-Rich Probabilistic Classifiers for NER Tasks. PKDD.
12. Su, K.-Y., M.-W. Wu and J.-S. Chang (1994). A Corpus-based Approach to Automatic Compound Extraction. Meeting of the Association for Computational Linguistics: 242-247.